One application in the manipulation of data used by computer and electronic devices is the compression and decompression of data. Storage space for data in memory devices is limited in many circumstances, so that data compression techniques are often used to reduce the amount of storage space that is needed for an image, a message, or other block of data. Once compressed and stored, the compressed data is eventually decompressed into its uncompressed, original form using a technique or scheme complementary to the compression technique. Some types of compression are known as lossy, where some data is lost in the compression and decompression process. However, in many applications, such as image compression, the lost data typically does not make a noticeable or practical difference in the final use or application of the data.
Some compression techniques (schemes) are well known. A transformation technique can be utilized to compress data, where the transformation technique helps separate an image (for example) into parts or sub-bands of differing importance, with respect to the image's visual quality. Some examples of well-known transformation techniques include the Discrete Cosine Transform (DCT) and the Discrete Fourier Transform. These types of techniques transform an image or other data from a spatial domain to the frequency domain.
Another transformation technique that has been used for compression is known as wavelet-based compression. In this type of compression, a wavelet transform is used to reduce the amount of data with little noticeable loss. One type of wavelet transform that can be performed using digital processors and circuits is the Discrete Wavelet Transform (DWT), which uses discrete samples of a continuous wavelet, and can be similar to a filtering technique with discrete coefficients. The DWT can be tuned to a particular application or input, allowing it in many cases to be more useful for applications such as image compression or enhancement than other transforms such as the discrete cosine transform (DCT) or averaging filters. For example, the JPEG2000 still image compression standard is wavelet-based. Most digital cameras are expected to move from DCT-based JPEG images to the wavelet-based JPEG2000 standard, since wavelet-based compression can achieve better image quality for a given compressed image size than conventional DCT-based compression methods. Once transformed by DWT and compressed, the compressed data can be decompressed to its original form and storage size using a complementary decompression mechanism and Inverse Discrete Wavelet Transform (IDWT).
In many applications, once images are compressed there is a requirement to compress them further and archive the compressed images. For example, in a digital video recording application, the user may wish to keep recorded images that cover a timespan of 30 days. In many instances, two days of high quality images and 28 days of lower quality images are acceptable. In another example, in the archival and storage of medical records or fingerprints, it is acceptable to keep some old records at a lower quality.
One problem with prior compression techniques is that they are inefficient when it comes to compressing data after the initial compression, e.g. for purposes of archival of data. For example, the prior art requires that to store compressed data at a higher compression ratio, the compressed data must be fully decompressed to its original form and then compressed at the higher ratio. It is a waste of time and processing resources to perform this decompression-compression process, especially when a large quantity of data needs to be archived at the higher compression ratio.